Research on Chinese Character Recognition Using Bag of Words

2010 ◽  
Vol 20-23 ◽  
pp. 395-400 ◽  
Author(s):  
Jia Ping Gui ◽  
Yi Zhou ◽  
Xin Da Lin ◽  
Kai Chen ◽  
Hai Bing Guan
Keyword(s):  
Image Recognition ◽  
Character Recognition ◽  
Chinese Character ◽  
Experimental Results ◽  
Bag Of Words ◽  
Accuracy Rate ◽  
Chinese Character Recognition ◽  
Complex Background

The traditional OCR obtains unsatisfactory results in the field of image recognition when images are processed in a complex background with low quality. This paper presents a novel application of the model of Bag of Words on Chinese character recognition, and extensively evaluated its effectiveness with 12 different fonts of Chinese character datasets under varying circumstances. Our experimental results demonstrate that this approach can achieve nearly 70% at its highest accuracy rate, which shows its performance far exceeds the traditional OCR’s

A Method of License Plate Chinese Character Recognition Based on Image Quality

2012 ◽  
Vol 424-425 ◽  
pp. 309-313
Author(s):  
Yu Feng Chen ◽  
Gang Yin
Keyword(s):  
Image Quality ◽  
Character Recognition ◽  
Minimum Distance ◽  
Chinese Character ◽  
Recognition Rate ◽  
Experimental Results ◽  
License Plate ◽  
Chinese Character Recognition ◽  
Recognition Result ◽  
Fuzzy Degree

This paper presents a method of license plate Chinese character recognition based on image quality for improving the recognition rate of low-resolution characters in license plates. Each subset of license plate Chinese character images with the same fuzzy degree are used to construct a corresponding PCA subspace, then the projection residuals distance between the Chinese character to be recognized and each subspace in the specific subspaces which are selected by character’s fuzzy degree is calculated. The class which corresponds to the subspace with the minimum distance is the recognition result. The experimental results show that the proposed method has higher recognition rate and faster recognition speed comparing to other ones.

scholarly journals Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition

2021 ◽  
Author(s):  
Jingye Chen ◽  
Bin Li ◽  
Xiangyang Xue
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Experimental Results ◽  
Research Interest ◽  
Chinese Characters ◽  
Specific Character ◽  
Know How ◽  
Chinese Character Recognition ◽  
Test Sets ◽  
Stroke Sequence

Chinese character recognition has attracted much research interest due to its wide applications. Although it has been studied for many years, some issues in this field have not been completely resolved yet, \textit{e.g.} the zero-shot problem. Previous character-based and radical-based methods have not fundamentally addressed the zero-shot problem since some characters or radicals in test sets may not appear in training sets under a data-hungry condition. Inspired by the fact that humans can generalize to know how to write characters unseen before if they have learned stroke orders of some characters, we propose a stroke-based method by decomposing each character into a sequence of strokes, which are the most basic units of Chinese characters. However, we observe that there is a one-to-many relationship between stroke sequences and Chinese characters. To tackle this challenge, we employ a matching-based strategy to transform the predicted stroke sequence to a specific character. We evaluate the proposed method on handwritten characters, printed artistic characters, and scene characters. The experimental results validate that the proposed method outperforms existing methods on both character zero-shot and radical zero-shot tasks. Moreover, the proposed method can be easily generalized to other languages whose characters can be decomposed into strokes.

A research on an intelligent recognition tool for bronze inscriptions of the Shang and Zhou dynasties

2020 ◽  
Vol 4 (4) ◽  
pp. 271-279
Author(s):  
Rui Guo
Keyword(s):  
Computer Technology ◽  
Character Recognition ◽  
Chinese Character ◽  
Research Process ◽  
Chinese Characters ◽  
Chinese Character Recognition ◽  
The World ◽  
Bronze Inscriptions ◽  
Intelligent Recognition ◽  
Functional Components

The intelligent recognition tool for bronze inscriptions of the Shang and Zhou dynasties—the “Shang Zhou Bronze Inscriptions Intelligent Mirror”—was successfully invented in Shanghai. This mirror, based on the computer technology of artificial intelligence (AI) image recognition and image retrieval, succeeds in automagical recognition of bronze inscriptions, both single letters and full texts. This research leads the trend of the AI recognition of Ancient Chinese characters and accumulates valuable experience for the development of inter-disciplinary research on Chinese character recognition. This essay emphasizes the importance of the bronze inscriptions of the Shang and Zhou dynasty database in the AI recognition of bronze inscriptions, introduces the functional components of this tool, and shares the whole research process in order to offer experience for the related research on AI recognition of other types of Ancient Chinese characters as well as ideographs in the world scope. “Shang Zhou Bronze Inscriptions Intelligent Mirror” as a tool for bronze inscription recognition also has room for improvement and support, and guidance from experts in similar areas is greatly welcomed.

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